New cases
Last season he only made one league appearance. His upward trajectory stalled massively, doubters said. In reality it was only to a more even level that should be expected of someone still in childhood degenevieve.com. Super critically, even not playing much senior football in a year left Nwaneri far ahead of his peers.
The constant counterargument is “that’s football” and the need to adapt, but Arteta’s staff would say that’s precisely the point. They talk of how Jurgen Klopp missed key players in 2020-21 and 2022-23 and tried to play the same way, but suffered drastic drop-offs. The same was true of Pep Guardiola in November, before Manchester City spent close to a quarter of a billion.
Sources have told ESPN that Real’s interest would increase should Xabi Alonso replace Carlo Ancelotti as head coach at the Santiago Bernabéu. But there remains uncertainty over Ancelotti’s future amid speculation he may not be in charge for Real’s Club World Cup campaign, which begins in June.
Arteta’s comments suggest he is looking for a player who can add a new dimension to Arsenal’s game, whether through creativity, versatility, or a proven track record in high-pressure situations. It’s not just about plugging holes; it’s about finding a player who can make a significant impact immediately upon arrival.
In the last few weeks he has overtaken him to become the real centre of attention. Operating in a modern full-back role that also sees him stride into the centre of the pitch, it would be wrong to say he doesn’t do what Nwaneri does.
Digital finds for connoisseurs
Facilitating fast and meaningful comparisons among small details from one or more artworks is the design goal of Erdmann’s ‘Morelli’s Vision’ technique. It derives its name in honour of Giovanni Morelli, an art historian who advocated for the careful study of small habitually-painted details to discern the characteristic ‘handwriting’ of an artist.Footnote 3 It is driven by a system of user- or computer-generated rectangular selections on artworks which are given a semantic tag such as ‘ear’ or ‘hand’. The model hinges on a recent breakthrough in computer vision and machine learning: Contrastive Language–Image Pre-training (CLIP) (Radford et al. 2021). This new approach makes it possible to jointly embed images and text within a high-dimensional semantic space in order to map out their degree of similarity relative to one another. This ability of the CLIP model, which we use without further specialised training on our images, to perform this task arises from the training procedure, in which it learns how to pair images with their original captions from an enormous set of image-caption pairs taken from the internet. To succeed at this task, the network must simultaneously ‘understand’ both how to read images as well as English text. While the details of the process are beyond the scope of this chapter, the key point is that the way the model does this is by learning how to compute an appropriate location in a high-dimensional embedding space for both images and captions. During training, the network is rewarded when, in this embedding space, the closest image to a given caption is the one it was originally paired with. Similarly, the network is rewarded if the closest caption to a given image is the one it was originally paired with. This implies that the network learns how to organise images (and captions) semantically within the space. Thus, the original objective of the CLIP model of comparing captions to images indirectly induces a means of comparing images with each other. In other words, images that are nearby each other in the embedding space would be well-described by the same set of captions.
The Curtain Viewer also features a system where every aspect of the view is encoded in the URL, enabling easy bookmarking of an exact configuration for later study or for sharing and collaboration. As a demonstration of the technology, every figure from the Bosch Catalogue Raisonné (Ilsink et al. 2016; Erdmann 2016a), is also presented online (Erdmann 2016b) using the Curtain Viewer, enabling readers to understand the exact context and details of every featured detail.
Facilitating fast and meaningful comparisons among small details from one or more artworks is the design goal of Erdmann’s ‘Morelli’s Vision’ technique. It derives its name in honour of Giovanni Morelli, an art historian who advocated for the careful study of small habitually-painted details to discern the characteristic ‘handwriting’ of an artist.Footnote 3 It is driven by a system of user- or computer-generated rectangular selections on artworks which are given a semantic tag such as ‘ear’ or ‘hand’. The model hinges on a recent breakthrough in computer vision and machine learning: Contrastive Language–Image Pre-training (CLIP) (Radford et al. 2021). This new approach makes it possible to jointly embed images and text within a high-dimensional semantic space in order to map out their degree of similarity relative to one another. This ability of the CLIP model, which we use without further specialised training on our images, to perform this task arises from the training procedure, in which it learns how to pair images with their original captions from an enormous set of image-caption pairs taken from the internet. To succeed at this task, the network must simultaneously ‘understand’ both how to read images as well as English text. While the details of the process are beyond the scope of this chapter, the key point is that the way the model does this is by learning how to compute an appropriate location in a high-dimensional embedding space for both images and captions. During training, the network is rewarded when, in this embedding space, the closest image to a given caption is the one it was originally paired with. Similarly, the network is rewarded if the closest caption to a given image is the one it was originally paired with. This implies that the network learns how to organise images (and captions) semantically within the space. Thus, the original objective of the CLIP model of comparing captions to images indirectly induces a means of comparing images with each other. In other words, images that are nearby each other in the embedding space would be well-described by the same set of captions.
The Curtain Viewer also features a system where every aspect of the view is encoded in the URL, enabling easy bookmarking of an exact configuration for later study or for sharing and collaboration. As a demonstration of the technology, every figure from the Bosch Catalogue Raisonné (Ilsink et al. 2016; Erdmann 2016a), is also presented online (Erdmann 2016b) using the Curtain Viewer, enabling readers to understand the exact context and details of every featured detail.
Belland (1991) and others have proposed connaisseurship as an alternative to more traditional methods for understanding teaching and learning. This chapter further explores the potential of connoisseurship in research, evaluation, and design for learning experiences. It argues that applying connoisseurship leads to a broader understanding of the many qualities and impacts of a learning experience, exploring influences and outcomes often left uncovered. After briefly examining examples of connoisseurship in dramatic criticism, wine tasting, and travel writing, drawing conclusions about their methods for understanding the complexities of experience, it suggest applications of connoisseurship in educational technology. Connoisseurs immerse themselves in experiences to examine subjective reactions, examine the historicity of the object of appreciation, attend to all details of an experience for possible significance, and take time to savor those details, trusting in their abilities to find the meaning that can arise only after a period of immersion and reflection.
Meaningful comparisons between artworks or between different areas of an artwork are essential to the expert’s judgement. Even with a collection of consistent colour-managed high-resolution images, traditional image-editing tools such as Photoshop are ill-suited to making frictionless comparisons among many works or among different imaging modalities of a single work. The problem is exacerbated when the images themselves are very large; 20 μm/pixel resolution (1270 ppi) 16-bit colour imaging consumes 15 GB/m2, so large-format paintings such as Hals’ militia company portraits or Rembrandt’s Night Watch consume hundreds of gigabytes each. Side-by-side comparisons of such artworks may then be practically impossible using standard image-editing software due to memory limitations. Furthermore, such an approach makes it very difficult to save a comparison for later review, and collaborative inspections are impractical.
Fresh items from the update
Get ready to elevate your raiding strategies with the new Siege Tower, a game-changing addition to the Primitive mode arsenal. This rolling fortress is not just a tool but a tactical advantage, allowing you to approach and infiltrate enemy bases with both cover and style.
Placing additional leaf litter on an existing one increases its size. The block can be positioned in four orientations and changes color based on the biome. Players can obtain leaf litter by smelting any type of leaf block, and it can also be used as fuel, though it only smelts half an item.
Along with the AP item changes, there are some champion changes as well. Cho’Gath, Senna, and Smolder are getting buffed. On the other hand, strong champions like Fiddlesticks, Xin Zhao, and Lulu are getting nerfed.
Get ready to elevate your raiding strategies with the new Siege Tower, a game-changing addition to the Primitive mode arsenal. This rolling fortress is not just a tool but a tactical advantage, allowing you to approach and infiltrate enemy bases with both cover and style.
Placing additional leaf litter on an existing one increases its size. The block can be positioned in four orientations and changes color based on the biome. Players can obtain leaf litter by smelting any type of leaf block, and it can also be used as fuel, though it only smelts half an item.
Along with the AP item changes, there are some champion changes as well. Cho’Gath, Senna, and Smolder are getting buffed. On the other hand, strong champions like Fiddlesticks, Xin Zhao, and Lulu are getting nerfed.